dtensor-modules

name	dtensor_modules
description	Implement DTensor-based context-parallel model modules that mirror a serial reference. Maps each serial layer/module to its Boltz-CP counterpart with exact input/output shapes, placements, collectives, and backward memory budget; writes the CP implementation following the autograd.Function conventions (explicit collectives, no implicit DTensor ops, promote_types, explicit from_local shape/stride, attribute-name + registration-order mirroring); and verifies it against the serial forward+backward via the test skill. Use when porting a custom model's trunk / attention / triangle / OPM / diffusion / confidence / loss modules to CP.
argument-hint	[module name] [2d]

dtensor_modules — port a serial module to context parallelism

The serial module is the mathematical specification. The CP version must produce numerically equivalent forward+backward while holding only its O(N/cp0) (single) or O(N²/(cp0·cp1)) (pair) shard. The serial→CP dictionary with all I/O placements, collectives, and backward budgets is in module_map.md — look up the target there first.

Preconditions

• docs/current_code_structure.md §4 (module map) names the target's serial block and its CP-candidate status (reuse / adapt / new).
• docs/cp_infra.md gives the 2D mesh and world_size.
• The features the module consumes are (or will be) produced by /fold-cp:shard_data_feats at known placements.
• Tech guide section for the target (see module_map.md).

Step 1 — Look up the contract

In module_map.md find the target's row: serial file, 2D CP file, tech guide §, input placements, output placements, collectives, and backward memory budget. If the target is in the substrate-primitive catalog, prefer composing it from those utilities over writing a new autograd.Function.

A reference CP layer = an app-agnostic ALGORITHM + a reference-SPECIFIC placement contract; reconcile the contract before reusing it. The Boltz file's ring / skew / gather algorithm is reusable, but the axes it shards vs replicates are Boltz's data contract and may differ from your model's (e.g. Boltz shards MSA-depth S where your model replicates it; a different window-edge convention). Blindly vendoring the core imports fine but fails parity. So: check the reference core's placement contract against YOUR data placements (from shard_data_feats / current_code_structure.md §3) first; if they match, reuse it; if they differ, reuse the algorithm but re-derive the collective schedule for your contract. Algorithm-reuse and contract-match are orthogonal — a row marked "reuse" can still need a contract re-derivation.

Step 2 — Extract the math spec AND the full input contract

Read the serial module (the user's, and the Boltz serial twin if it maps cleanly). Record:

• the exact math (so the CP forward mirrors it line-for-line where possible);
• the complete input contract — including feature-dict fields the signature does not spell out. Modules routinely pull extra tensors out of a feats dict (masks, atom→token indices, bias tensors, ensemble indices). Each such field has a required placement; cross-check it against the data-feature inventory (current_code_structure.md §3) and the placement dictionary from shard_data_feats. An undocumented feature input at the wrong placement is the most common CP integration bug.

Step 3 — Decide compose vs fuse

• ≤3 chained DTensor ops: compose the substrate utilities (elementwise_op, sharded_op, shardwise_op, …) — each checks placements at its boundary; flexible and auditable. Canonical: modules/encoders.py:_atom_encoder.
• ≥4 chained ops: fuse into one autograd.Function — to_local() once, plain PyTorch math, explicit collectives, one from_local(). Canonical: loss/distogram.py.

Step 4 — Implement (autograd.Function conventions — these are hard rules)

Forward:

• Assert inputs are DTensors of the expected placements and global shapes — accept only DTensor on the distributed path (no "naked" plain tensors, which hide wrong placements/strides — Rule 6); assert all share one device_mesh; assert even sharding for every Shard (collectives need equal per-rank buffer shapes — Rule 8); reject unhandled Partial. Do not read ctx.needs_input_grad in forward (use tensor.requires_grad).
• Decorate @torch.amp.custom_fwd(device_type="cuda") / custom_bwd, then audit the dtype path end-to-end against the app's precision semantics (Rule 19): operands cast to the compute dtype before math, every communicated buffer pinned to one dtype across ranks (a per-rank/autocast-dependent dtype deadlocks send/recv), and no unintended up/down-cast (correctness-silent but a transient peak-memory hazard — see mem_profile).
• Insert collectives explicitly (the exact set from module_map.md); make tensors contiguous before collectives.
• torch.promote_types(dtype, torch.float32), never .float().
• Build outputs with DTensor.from_local(..., shape=<global>, stride=<global via update_exhaustive_strides>, placements=<...>) — never let it infer (Rule 9).

Backward:

• Match upstream-grad dtype to the saved-tensor dtype before mixed-precision math; cast operands to compute dtype for CPU.
• Reduce parameter gradients in ≥fp32 (_all_reduce_grad_gteqfp32).
• Saved-for-backward tensors must obey the forward per-rank budget — a tensor that scales with full N or S is a critical bug (Rule 11).

Module wiring:

• Mirror the serial attribute names and __init__ registration order exactly, so named_parameters()/state_dict() line up in parity tests.
• In the model forward, avoid native DTensor dispatch on a sharded axis (zeros_like, reshape, repeat_interleave, squeeze) — use dtensor_zeros, to_local()/from_local(), or the substrate utilities. Elementwise .to(dtype) is safe.
• Broadcast control-flow scalars (recycle/sample counts) across the flat CP group so all ranks branch identically (Rule 7).
• Document input/output placements, collectives, and backward budget in the module docstring (template: loss/distogram.py).

Step 5 — Wire into the forward and reconcile neighbors

Insert the module into the model's distributed forward mirroring the serial order. Confirm the producer→consumer placement contract with each neighbor: the upstream module's output placement must equal this module's input placement (Rule 4). If you are porting several coupled modules, this reconciliation is the job /fold-cp:dispatch_work enforces between paired agents.

Step 6 — Test against the serial reference

Hand to /fold-cp:test at the right level:

• unit — the single op/autograd.Function vs its serial twin;
• layer integration — the layer in isolation;
• module integration — the module with its real feature inputs drawn from the shared synthetic DataModule (one Dataset/DataLoader/DataModule that reuses the production distributor; tests subset the feature set), not ad-hoc per-feature randint, so the test also exercises the featurizer→module seam (Rule 4) — see /fold-cp:test Step 2.2;
• workflow — end-to-end forward (and backward/step) under CP.

Always serial fwd+bwd as ground truth, explicit random grad_output, fp64, and the anti-vacuous checks. Pin each new operator with its own parity test before moving downstream.

Output contract

• The CP module implemented per the conventions above, mirroring serial attribute names/order, with a docstring documenting placements + collectives + backward budget.
• A passing parity test at the appropriate level (serial == CP fwd and bwd), logged.
• The module's row in docs/current_code_structure.md §4 updated to "done", and any newly-resolved feature-input placements written back to §3.